def run_evaluator_on_latest_model(dataset, config):
config = configurations_qa[config](dataset)
latest_model = get_latest_model(
os.path.join(config["training"]["basepath"],
config["training"]["exp_dirname"]))
evaluator = Evaluator(dataset, latest_model)
_ = evaluator.evaluate(dataset.test_data, save_results=True)
return evaluator
def generate_graphs_on_latest_model(dataset, config):
config = configurations_qa[config](dataset)
latest_model = get_latest_model(os.path.join(config["training"]["basepath"], config["training"]["exp_dirname"]))
if latest_model is not None:
evaluator = Evaluator(dataset, latest_model)
_ = evaluator.evaluate(dataset.test_data, save_results=True, is_embds=False)
print('outside eval')
generate_graphs(dataset, config["training"]["exp_dirname"], evaluator.model, test_data=dataset.test_data)
def train_dataset_and_get_atn_map(dataset, encoders, num_iters=15):
for e in encoders:
config = configurations_qa[e](dataset)
trainer = Trainer(dataset, config=config, _type=dataset.trainer_type)
trainer.train(dataset.train_data,
dataset.dev_data,
n_iters=num_iters,
save_on_metric=dataset.save_on_metric)
# Get train losses as well?
evaluator = Evaluator(dataset, trainer.model.dirname)
_, attentions, scores = evaluator.evaluate(dataset.test_data,
save_results=True)
return scores, attentions
def train_dataset(dataset, config):
try:
config = configurations_qa[config](dataset)
n_iters = dataset.n_iters if hasattr(dataset, "n_iters") else 25
trainer = Trainer(dataset, config=config, _type=dataset.trainer_type)
trainer.train(dataset.train_data,
dataset.dev_data,
n_iters=n_iters,
save_on_metric=dataset.save_on_metric)
evaluator = Evaluator(dataset, trainer.model.dirname)
_ = evaluator.evaluate(dataset.test_data, save_results=True)
return trainer, evaluator
except Exception as e:
print(e)
return
def generate_graphs_on_latest_model(dataset, config):
try:
config = configurations_qa[config](dataset)
latest_model = get_latest_model(
os.path.join(config['training']['basepath'],
config['training']['exp_dirname']))
if latest_model is not None:
evaluator = Evaluator(dataset, latest_model)
_ = evaluator.evaluate(dataset.test_data, save_results=True)
generate_graphs(dataset,
config['training']['exp_dirname'],
evaluator.model,
test_data=dataset.test_data)
except:
return
def generate_graphs_on_latest_model(dataset, config):
print("GENERATING GRAPHS FOR EXPERIMENT ON LATEST MODEL")
config = configurations_qa[config](dataset)
latest_model = get_latest_model(
os.path.join(config["training"]["basepath"],
config["training"]["exp_dirname"]))
if latest_model is not None:
evaluator = Evaluator(dataset, latest_model)
_ = evaluator.evaluate(dataset.test_data,
save_results=True,
is_embds=False)
print("outside eval")
generate_graphs(
dataset,
config["training"]["exp_dirname"],
evaluator.model,
test_data=dataset.test_data,
)
